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Competitor Radar
by
xiaohuaishu
· GitHub ↗
· v1.0.0
· MIT-0
363
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0
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Install in OpenClaw
/install competitor-radar
Description
竞品动态监控雷达。自动抓取竞品博客RSS、GitHub Release、HackerNews讨论，用AI评分筛选重要动态，生成结构化报告。当需要了解竞品最新动向、监控行业变化时使用。
README (SKILL.md)
Competitor Radar 🎯
自动监控竞品动态，生成结构化分析报告。
配置竞品
编辑 config.yaml，添加你要监控的竞品：
competitors:
- name: "竞品名称"
domain: "example.com"
blog_rss: "https://example.com/rss.xml"
github_org: "github-org-name"
keywords: ["关键词1", "关键词2"]
使用方法
# 扫描过去7天
python3 radar.py
# 扫描过去14天，保存报告
python3 radar.py --days 14 --output report.md
# 跳过AI评分（更快）
python3 radar.py --no-ai
# 使用自定义配置
python3 radar.py --config /path/to/my-config.yaml
数据来源
- 官网博客 RSS Feed
- GitHub Release / Tag
- HackerNews 提及
输出格式
Markdown 报告，按重要性分级（🔴重要 / 🟡值得关注 / ⚪一般）
Usage Guidance
Do not install or run this skill without review. The code contains a hard-coded LLM API key and local LLM endpoint (embedded secret) and also uses an optional GITHUB_TOKEN environment variable that is not documented. This is suspicious because secrets should not be hard-coded in distributed code. Before using: (1) inspect radar.py and _write_radar.py yourself (or with a dev) and remove any embedded API keys, replacing them with environment-configured values; (2) supply your own LLM endpoint/key via environment variables or local config and confirm the endpoint is trusted; (3) be aware the script will make network requests to RSS feeds, api.github.com, hn.algolia.com and to the configured LLM endpoint; (4) if you do not control or recognize the embedded key, treat it as potentially compromised and do not expose sensitive data through the skill; (5) prefer running it in an isolated environment (non-privileged user, network-restricted) until you have sanitized the code. If you want, provide the full untruncated radar.py/_write_radar.py and I can point to the exact lines to change.
Capability Analysis
Type: OpenClaw Skill
Name: competitor-radar
Version: 1.0.0
The skill bundle is a legitimate tool for monitoring competitor activities via RSS feeds, GitHub releases, and HackerNews. The logic in `radar.py` and the helper script `_write_radar.py` is transparent and aligns with the stated purpose in `SKILL.md`. While the code contains a hardcoded API key and references a local LLM proxy (127.0.0.1:18790), these appear to be environment-specific configurations for the OpenClaw platform rather than malicious artifacts. No evidence of data exfiltration, unauthorized file access, or prompt injection was found.
Capability Assessment
Purpose & Capability
The stated purpose (monitor blogs, GitHub, HackerNews and produce reports) matches the code's fetching and reporting behavior, and requiring python3 is reasonable. However, the code embeds a hard-coded LLM API key and a local LLM endpoint (http://127.0.0.1:18790) directly in the scripts instead of using a declared/optional environment variable. Embedding a key in the code is disproportionate to the stated purpose and not documented in SKILL.md or requires.env.
Instruction Scope
SKILL.md only instructs running radar.py with an optional config and --no-ai, but the runtime code will call external services (GitHub API, hn.algolia, blogs) and a local LLM endpoint using a hard-coded API key. The instructions do not mention the LLM endpoint, the embedded API key, or optional env vars (e.g., GITHUB_TOKEN), so the runtime behavior is under-documented and gives the skill more network capability than the instructions disclose.
Install Mechanism
No install spec; the skill is instruction-and-code-only and only requires python3 on PATH. This is low install risk because nothing is downloaded during install.
Credentials
The declared metadata lists no required environment variables, but the code optionally reads GITHUB_TOKEN and unambiguously contains a hard-coded LLM API key and endpoint in both radar.py and _write_radar.py. Requiring or shipping credentials in-code is not proportional: credentials should be optional and provided via environment variables or config, and any required tokens should be declared in the skill metadata.
Persistence & Privilege
always is false and there are no install hooks or modifications to other skills or system-wide settings. The skill can be invoked by the agent autonomously (default), which is expected for skills; that alone is not a concern here.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install competitor-radar - After installation, invoke the skill by name or use
/competitor-radar - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
初始发布：自动监控竞品博客RSS、GitHub Release、HackerNews动态，AI评分筛选重要信息
Metadata
Frequently Asked Questions
What is Competitor Radar?
竞品动态监控雷达。自动抓取竞品博客RSS、GitHub Release、HackerNews讨论，用AI评分筛选重要动态，生成结构化报告。当需要了解竞品最新动向、监控行业变化时使用。 It is an AI Agent Skill for Claude Code / OpenClaw, with 363 downloads so far.
How do I install Competitor Radar?
Run "/install competitor-radar" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Competitor Radar free?
Yes, Competitor Radar is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Competitor Radar support?
Competitor Radar is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Competitor Radar?
It is built and maintained by xiaohuaishu (@xiaohuaishu); the current version is v1.0.0.
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